公开(公告)号：US20210073028A1

公开(公告)日：2021-03-11

申请号：US16600437

申请日：2019-10-11

Applicant: Google LLC

Inventor： Sheng Li ,  Brian Zhang ,  Liqun Cheng ,  Norman Paul Jouppi ,  Yun Ni

IPC: G06F9/48 ,  G06F9/54 ,  G06F9/50 ,  G06F11/36 ,  G06K9/62 ,  G06N3/08 ,  G06F11/34

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for scheduling operations represented as a computational graph on a distributed computing network. A method includes: receiving data representing operations to be executed in order to perform a job on a plurality of hardware accelerators of a plurality of different accelerator types; generating, for the job and from at least the data representing the operations, features that represent a predicted performance for the job on hardware accelerators of the plurality of different accelerator types; generating, from the features, a respective predicted performance metric for the job for each of the plurality of different accelerator types according to a performance objective function; and providing, to a scheduling system, one or more recommendations for scheduling the job on one or more recommended types of hardware accelerators.

公开(公告)号：US20250077833A1

公开(公告)日：2025-03-06

申请号：US18821971

申请日：2024-08-30

Applicant: Google LLC

Inventor： Sheng Li ,  Norman Paul Jouppi ,  Quoc V. Le ,  Mingxing Tan ,  Ruoming Pang ,  Liqun Cheng ,  Andrew Li

IPC: G06N3/04 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining an architecture for a task neural network that is configured to perform a particular machine learning task on a target set of hardware resources. When deployed on a target set of hardware, such as a collection of datacenter accelerators, the task neural network may be capable of performing the particular machine learning task with enhanced accuracy and speed.

公开(公告)号：US12131244B2

公开(公告)日：2024-10-29

申请号：US17039178

申请日：2020-09-30

Applicant: Google LLC

Inventor： Sheng Li ,  Norman Paul Jouppi ,  Quoc V. Le ,  Mingxing Tan ,  Ruoming Pang ,  Liqun Cheng ,  Andrew Li

IPC: G06N3/04 ,  G06N3/08

CPC classification number: G06N3/04 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining an architecture for a task neural network that is configured to perform a particular machine learning task on a target set of hardware resources. When deployed on a target set of hardware, such as a collection of datacenter accelerators, the task neural network may be capable of performing the particular machine learning task with enhanced accuracy and speed.

公开(公告)号：US20240037373A1

公开(公告)日：2024-02-01

申请号：US17875594

申请日：2022-07-28

Applicant: Google LLC

Inventor： Sheng Li ,  Norman Paul Jouppi ,  Garrett Axel Andersen ,  Quoc V. Le ,  Liqun Cheng ,  Parthasarathy Ranganathan

IPC: G06N3/04 ,  G06N3/063

CPC classification number: G06N3/0454 ,  G06N3/063

Abstract: Aspects of the disclosure are directed to jointly searching machine learning model architectures and hardware architectures in a combined space of models, hardware, and mapping strategies. A search strategy is utilized where all models, hardware, and mappings are evaluated together at once via weight sharing and a supernetwork. A multi-objective reward function is utilized with objectives for quality, performance, power, and area.

公开(公告)号：US20220230048A1

公开(公告)日：2022-07-21

申请号：US17175029

申请日：2021-02-12

Applicant: Google LLC

Inventor： Andrew Li ,  Sheng Li ,  Mingxing Tan ,  Ruoming Pang ,  Liqun Cheng ,  Quoc V. Le ,  Norman Paul Jouppi

IPC: G06N3/04 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer-readable media, for scaling neural network architectures on hardware accelerators. A method includes receiving training data and information specifying target computing resources, and performing using the training data, a neural architecture search over a search space to identify an architecture for a base neural network. A plurality of scaling parameter values for scaling the base neural network can be identified, which can include repeatedly selecting a plurality of candidate scaling parameter values, and determining a measure of performance for the base neural network scaled according to the plurality of candidate scaling parameter values, in accordance with a plurality of second objectives including a latency objective. An architecture for a scaled neural network can be determined using the architecture of the base neural network scaled according to the plurality of scaling parameter values.

公开(公告)号：US20230297580A1

公开(公告)日：2023-09-21

申请号：US17721873

申请日：2022-04-15

Applicant: Google LLC

Inventor： Sheng Li ,  Garrett Axel Andersen ,  Norman Paul Jouppi ,  Quoc V. Le ,  Liqun Cheng ,  Parthasarathy Ranganathan ,  Julian Paul Grady ,  Yang Li ,  Martin Wicke ,  Yifeng Lu ,  Yun Ni ,  Kun Wang

IPC: G06F16/2457 ,  G06F16/2455 ,  G06N3/063

CPC classification number: G06F16/2457 ,  G06F16/24554 ,  G06N3/063

Abstract: According to various implementations, generally disclosed herein is a hybrid and hierarchical neural architecture search (NAS) approach. The approach includes performing a search space partitioning scheme to divide the search space into sub-search spaces. The approach further includes performing a first type of NAS, such as a Multi-trial NAS, to cover a search across the sub-search spaces. The approach also includes performing a second type of NAS, such as a One-Shot NAS, to cover each sub-search space. The approach further includes automatically stopping the second type of NAS based on one or more early stopping criteria.

公开(公告)号：US20230222000A1

公开(公告)日：2023-07-13

申请号：US18091951

申请日：2022-12-30

Applicant: Google LLC

Inventor： Sheng Li ,  Brian Zhang ,  Liqun Cheng ,  Norman Paul Jouppi ,  Yun Ni

IPC: G06F9/50 ,  G06F9/54 ,  G06F11/36 ,  G06N3/08 ,  G06F11/34 ,  G06F9/48

CPC classification number: G06F9/5044 ,  G06F9/545 ,  G06F11/3612 ,  G06F9/5066 ,  G06F18/214 ,  G06N3/08 ,  G06F9/5011 ,  G06F11/3409 ,  G06F9/4881 ,  G06F2209/501

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for scheduling operations represented as a computational graph on a distributed computing network. A method includes: receiving data representing operations to be executed in order to perform a job on a plurality of hardware accelerators of a plurality of different accelerator types; generating, for the job and from at least the data representing the operations, features that represent a predicted performance for the job on hardware accelerators of the plurality of different accelerator types; generating, from the features, a respective predicted performance metric for the job for each of the plurality of different accelerator types according to a performance objective function; and providing, to a scheduling system, one or more recommendations for scheduling the job on one or more recommended types of hardware accelerators.

公开(公告)号：US20240231667A1

公开(公告)日：2024-07-11

申请号：US18152428

申请日：2023-01-10

Applicant: Google LLC

Inventor： Sheng Li ,  Sridhar Lakshmanamurthy ,  Norman Paul Jouppi ,  Martin Guy Dixon ,  Daniel Stodolsky ,  Quoc V. Le ,  Liqun Cheng ,  Erik Karl Norden ,  Parthasarathy Ranganathan

IPC: G06F3/06

CPC classification number: G06F3/0647 ,  G06F3/0611 ,  G06F3/067

Abstract: Aspects of the disclosure are directed to a heterogeneous machine learning accelerator system with compute and memory nodes connected by high speed chip-to-chip interconnects. While existing remote/disaggregated memory may require memory expansion via remote processing units, aspects of the disclosure add memory nodes into machine learning accelerator clusters via the chip-to-chip interconnects without needing assistance from remote processing units to achieve higher performance, simpler software stack, and/or lower cost. The memory nodes may support prefetch and intelligent compression to enable the use of low cost memory without performance degradation.

公开(公告)号：US11544105B2

公开(公告)日：2023-01-03

申请号：US16600437

申请日：2019-10-11

Applicant: Google LLC

Inventor： Sheng Li ,  Brian Zhang ,  Liqun Cheng ,  Norman Paul Jouppi ,  Yun Ni

IPC: G06F9/46 ,  G06F9/48 ,  G06F9/50 ,  G06F9/54 ,  G06F11/34 ,  G06F11/36 ,  G06K9/62 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for scheduling operations represented as a computational graph on a distributed computing network. A method includes: receiving data representing operations to be executed in order to perform a job on a plurality of hardware accelerators of a plurality of different accelerator types; generating, for the job and from at least the data representing the operations, features that represent a predicted performance for the job on hardware accelerators of the plurality of different accelerator types; generating, from the features, a respective predicted performance metric for the job for each of the plurality of different accelerator types according to a performance objective function; and providing, to a scheduling system, one or more recommendations for scheduling the job on one or more recommended types of hardware accelerators.

公开(公告)号：US20220019869A1

公开(公告)日：2022-01-20

申请号：US17039178

申请日：2020-09-30

Applicant: Google LLC

Inventor： Sheng Li ,  Norman Paul Jouppi ,  Quoc V. Le ,  Mingxing Tan ,  Ruoming Pang ,  Liqun Cheng ,  Andrew Li

IPC: G06N3/04 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining an architecture for a task neural network that is configured to perform a particular machine learning task on a target set of hardware resources. When deployed on a target set of hardware, such as a collection of datacenter accelerators, the task neural network may be capable of performing the particular machine learning task with enhanced accuracy and speed.